1 Análisis PPM-S

library(sjPlot)
library(dplyr)
library(lavaan)

data01 <- sjlabelled::read_spss(path = "data/Estudio_3_ola1_January_9_2020.sav",verbose = FALSE)

dat01 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv01")) %>% na.omit()
dat02 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv02")) %>% na.omit()
dat03 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv03_p")) %>% na.omit()

1.1 Version 01:

  1. Percepcion esfuerzo
  2. Percepcion talento
  3. Percepcion familia rica
  4. Percepcion redes
  5. Preferencia esfuerzo
  6. Preferencia talento
  7. Preferencia familia rica
  8. Preferencia redes
model01 <- 'perc_merit=~meritv01_perc_effort+meritv01_perc_talent 
            perc_nmerit=~meritv01_perc_wpart+meritv01_perc_netw  
            pref_merit=~meritv01_pref_effort+meritv01_pref_talent 
            pref_nmerit=~meritv01_pref_wpart+meritv01_pref_netw'

fit1 <- cfa(model = model01,data = dat01,ordered = c("meritv01_perc_effort","meritv01_perc_talent",
                                                     "meritv01_perc_wpart","meritv01_perc_netw",
                                                     "meritv01_pref_effort","meritv01_pref_talent",
                                                     "meritv01_pref_wpart","meritv01_pref_netw"))
## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative
summary(fit1,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit1,what = "std",thresholds = FALSE, intercepts = FALSE)

## lavaan 0.6-4 ended normally after 55 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                         46
## 
##   Number of observations                           712
## 
##   Estimator                                       DWLS      Robust
##   Model Fit Test Statistic                      25.631      42.276
##   Degrees of freedom                                14          14
##   P-value (Chi-square)                           0.029       0.000
##   Scaling correction factor                                  0.650
##   Shift parameter                                            2.823
##     for simple second-order correction (Mplus variant)
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             5947.085    4084.523
##   Degrees of freedom                                28          28
##   P-value                                        0.000       0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.998       0.993
##   Tucker-Lewis Index (TLI)                       0.996       0.986
## 
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.034       0.053
##   90 Percent Confidence Interval          0.011  0.055       0.035  0.072
##   P-value RMSEA <= 0.05                          0.890       0.354
## 
##   Robust RMSEA                                                  NA
##   90 Percent Confidence Interval                                NA     NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.032       0.032
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model        Unstructured
##   Standard Errors                           Robust.sem
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit =~                                                         
##     mrtv01_prc_ffr    1.000                               0.689    0.689
##     mrtv01_prc_tln    1.177    0.100   11.799    0.000    0.810    0.810
##   perc_nmerit =~                                                        
##     mrtv01_prc_wpr    1.000                               0.850    0.850
##     mrtv01_prc_ntw    1.102    0.061   18.198    0.000    0.936    0.936
##   pref_merit =~                                                         
##     mrtv01_prf_ffr    1.000                               0.848    0.848
##     mrtv01_prf_tln    0.758    0.053   14.407    0.000    0.643    0.643
##   pref_nmerit =~                                                        
##     mrtv01_prf_wpr    1.000                               0.545    0.545
##     mrtv01_prf_ntw    2.302    0.754    3.054    0.002    1.255    1.255
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit ~~                                                         
##     perc_nmerit       0.016    0.027    0.603    0.546    0.028    0.028
##     pref_merit        0.326    0.033    9.966    0.000    0.559    0.559
##     pref_nmerit       0.064    0.028    2.317    0.020    0.172    0.172
##   perc_nmerit ~~                                                        
##     pref_merit        0.379    0.034   11.290    0.000    0.526    0.526
##     pref_nmerit      -0.035    0.021   -1.684    0.092   -0.077   -0.077
##   pref_merit ~~                                                         
##     pref_nmerit       0.023    0.020    1.150    0.250    0.051    0.051
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .mrtv01_prc_ffr    0.000                               0.000    0.000
##    .mrtv01_prc_tln    0.000                               0.000    0.000
##    .mrtv01_prc_wpr    0.000                               0.000    0.000
##    .mrtv01_prc_ntw    0.000                               0.000    0.000
##    .mrtv01_prf_ffr    0.000                               0.000    0.000
##    .mrtv01_prf_tln    0.000                               0.000    0.000
##    .mrtv01_prf_wpr    0.000                               0.000    0.000
##    .mrtv01_prf_ntw    0.000                               0.000    0.000
##     perc_merit        0.000                               0.000    0.000
##     perc_nmerit       0.000                               0.000    0.000
##     pref_merit        0.000                               0.000    0.000
##     pref_nmerit       0.000                               0.000    0.000
## 
## Thresholds:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     mrtv01_prc_f|1   -1.023    0.057  -17.909    0.000   -1.023   -1.023
##     mrtv01_prc_f|2   -0.401    0.048   -8.290    0.000   -0.401   -0.401
##     mrtv01_prc_f|3    0.120    0.047    2.546    0.011    0.120    0.120
##     mrtv01_prc_f|4    0.809    0.053   15.251    0.000    0.809    0.809
##     mrtv01_prc_t|1   -1.259    0.063  -19.863    0.000   -1.259   -1.259
##     mrtv01_prc_t|2   -0.417    0.049   -8.587    0.000   -0.417   -0.417
##     mrtv01_prc_t|3    0.444    0.049    9.105    0.000    0.444    0.444
##     mrtv01_prc_t|4    1.324    0.066   20.211    0.000    1.324    1.324
##     mrtv01_prc_w|1   -1.192    0.061  -19.416    0.000   -1.192   -1.192
##     mrtv01_prc_w|2   -0.775    0.053  -14.762    0.000   -0.775   -0.775
##     mrtv01_prc_w|3   -0.227    0.047   -4.790    0.000   -0.227   -0.227
##     mrtv01_prc_w|4    0.397    0.048    8.216    0.000    0.397    0.397
##     mrtv01_prc_n|1   -1.275    0.064  -19.955    0.000   -1.275   -1.275
##     mrtv01_prc_n|2   -0.943    0.055  -17.012    0.000   -0.943   -0.943
##     mrtv01_prc_n|3   -0.463    0.049   -9.475    0.000   -0.463   -0.463
##     mrtv01_prc_n|4    0.448    0.049    9.179    0.000    0.448    0.448
##     mrtv01_prf_f|1   -1.405    0.068  -20.530    0.000   -1.405   -1.405
##     mrtv01_prf_f|2   -1.104    0.059  -18.695    0.000   -1.104   -1.104
##     mrtv01_prf_f|3   -0.564    0.050  -11.313    0.000   -0.564   -0.564
##     mrtv01_prf_f|4    0.216    0.047    4.566    0.000    0.216    0.216
##     mrtv01_prf_t|1   -1.316    0.065  -20.170    0.000   -1.316   -1.316
##     mrtv01_prf_t|2   -0.729    0.052  -14.056    0.000   -0.729   -0.729
##     mrtv01_prf_t|3    0.188    0.047    3.968    0.000    0.188    0.188
##     mrtv01_prf_t|4    0.966    0.056   17.273    0.000    0.966    0.966
##     mrtv01_prf_w|1   -0.738    0.052  -14.198    0.000   -0.738   -0.738
##     mrtv01_prf_w|2   -0.106    0.047   -2.247    0.025   -0.106   -0.106
##     mrtv01_prf_w|3    0.829    0.053   15.528    0.000    0.829    0.829
##     mrtv01_prf_w|4    1.793    0.088   20.388    0.000    1.793    1.793
##     mrtv01_prf_n|1   -0.610    0.050  -12.115    0.000   -0.610   -0.610
##     mrtv01_prf_n|2    0.134    0.047    2.845    0.004    0.134    0.134
##     mrtv01_prf_n|3    1.110    0.059   18.753    0.000    1.110    1.110
##     mrtv01_prf_n|4    2.005    0.104   19.276    0.000    2.005    2.005
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .mrtv01_prc_ffr    0.526                               0.526    0.526
##    .mrtv01_prc_tln    0.343                               0.343    0.343
##    .mrtv01_prc_wpr    0.278                               0.278    0.278
##    .mrtv01_prc_ntw    0.123                               0.123    0.123
##    .mrtv01_prf_ffr    0.281                               0.281    0.281
##    .mrtv01_prf_tln    0.587                               0.587    0.587
##    .mrtv01_prf_wpr    0.703                               0.703    0.703
##    .mrtv01_prf_ntw   -0.576                              -0.576   -0.576
##     perc_merit        0.474    0.048    9.827    0.000    1.000    1.000
##     perc_nmerit       0.722    0.043   16.749    0.000    1.000    1.000
##     pref_merit        0.719    0.052   13.732    0.000    1.000    1.000
##     pref_nmerit       0.297    0.099    3.014    0.003    1.000    1.000
## 
## Scales y*:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     mrtv01_prc_ffr    1.000                               1.000    1.000
##     mrtv01_prc_tln    1.000                               1.000    1.000
##     mrtv01_prc_wpr    1.000                               1.000    1.000
##     mrtv01_prc_ntw    1.000                               1.000    1.000
##     mrtv01_prf_ffr    1.000                               1.000    1.000
##     mrtv01_prf_tln    1.000                               1.000    1.000
##     mrtv01_prf_wpr    1.000                               1.000    1.000
##     mrtv01_prf_ntw    1.000                               1.000    1.000

1.2 Version 02:

  1. Percepcion esfuerzo
  2. Preferencia esfuerzo
  3. Percepcion talento
  4. Preferencia talento
  5. Percepcion familia rica
  6. Preferencia familia rica
  7. Percepcion redes
  8. Preferencia redes
model02 <- 'perc_merit=~meritv02_perc_effort+meritv02_perc_talent 
            perc_nmerit=~meritv02_perc_wpart+meritv02_perc_netw  
            pref_merit=~meritv02_pref_effort+meritv02_pref_talent 
            pref_nmerit=~meritv02_pref_wpart+meritv02_pref_netw'

fit2 <- cfa(model = model02,data = dat02,ordered = c("meritv02_perc_effort","meritv02_perc_talent",
                                                     "meritv02_perc_wpart","meritv02_perc_netw",
                                                     "meritv02_pref_effort","meritv02_pref_talent",
                                                     "meritv02_pref_wpart","meritv02_pref_netw"))
## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative
summary(fit2,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit2,what = "std")

## lavaan 0.6-4 ended normally after 34 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                         46
## 
##   Number of observations                           717
## 
##   Estimator                                       DWLS      Robust
##   Model Fit Test Statistic                      67.652     107.573
##   Degrees of freedom                                14          14
##   P-value (Chi-square)                           0.000       0.000
##   Scaling correction factor                                  0.641
##   Shift parameter                                            1.952
##     for simple second-order correction (Mplus variant)
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             3301.803    2410.566
##   Degrees of freedom                                28          28
##   P-value                                        0.000       0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.984       0.961
##   Tucker-Lewis Index (TLI)                       0.967       0.921
## 
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.073       0.097
##   90 Percent Confidence Interval          0.056  0.091       0.080  0.114
##   P-value RMSEA <= 0.05                          0.013       0.000
## 
##   Robust RMSEA                                                  NA
##   90 Percent Confidence Interval                                NA     NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.050       0.050
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model        Unstructured
##   Standard Errors                           Robust.sem
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit =~                                                         
##     mrtv02_prc_ffr    1.000                               0.758    0.758
##     mrtv02_prc_tln    0.944    0.058   16.361    0.000    0.716    0.716
##   perc_nmerit =~                                                        
##     mrtv02_prc_wpr    1.000                               0.842    0.842
##     mrtv02_prc_ntw    0.965    0.100    9.614    0.000    0.812    0.812
##   pref_merit =~                                                         
##     mrtv02_prf_ffr    1.000                               0.816    0.816
##     mrtv02_prf_tln    0.795    0.053   14.957    0.000    0.649    0.649
##   pref_nmerit =~                                                        
##     mrtv02_prf_wpr    1.000                               1.044    1.044
##     mrtv02_prf_ntw    0.498    0.143    3.488    0.000    0.520    0.520
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit ~~                                                         
##     perc_nmerit       0.031    0.031    1.001    0.317    0.049    0.049
##     pref_merit        0.448    0.030   14.761    0.000    0.723    0.723
##     pref_nmerit       0.210    0.034    6.163    0.000    0.265    0.265
##   perc_nmerit ~~                                                        
##     pref_merit        0.295    0.035    8.353    0.000    0.430    0.430
##     pref_nmerit       0.085    0.035    2.396    0.017    0.097    0.097
##   pref_merit ~~                                                         
##     pref_nmerit       0.135    0.035    3.804    0.000    0.158    0.158
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .mrtv02_prc_ffr    0.000                               0.000    0.000
##    .mrtv02_prc_tln    0.000                               0.000    0.000
##    .mrtv02_prc_wpr    0.000                               0.000    0.000
##    .mrtv02_prc_ntw    0.000                               0.000    0.000
##    .mrtv02_prf_ffr    0.000                               0.000    0.000
##    .mrtv02_prf_tln    0.000                               0.000    0.000
##    .mrtv02_prf_wpr    0.000                               0.000    0.000
##    .mrtv02_prf_ntw    0.000                               0.000    0.000
##     perc_merit        0.000                               0.000    0.000
##     perc_nmerit       0.000                               0.000    0.000
##     pref_merit        0.000                               0.000    0.000
##     pref_nmerit       0.000                               0.000    0.000
## 
## Thresholds:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     mrtv02_prc_f|1   -0.943    0.055  -17.062    0.000   -0.943   -0.943
##     mrtv02_prc_f|2   -0.329    0.048   -6.892    0.000   -0.329   -0.329
##     mrtv02_prc_f|3    0.170    0.047    3.618    0.000    0.170    0.170
##     mrtv02_prc_f|4    0.785    0.052   14.960    0.000    0.785    0.785
##     mrtv02_prc_t|1   -1.303    0.065  -20.178    0.000   -1.303   -1.303
##     mrtv02_prc_t|2   -0.469    0.049   -9.626    0.000   -0.469   -0.469
##     mrtv02_prc_t|3    0.385    0.048    8.003    0.000    0.385    0.385
##     mrtv02_prc_t|4    1.328    0.065   20.300    0.000    1.328    1.328
##     mrtv02_prc_w|1   -1.175    0.061  -19.356    0.000   -1.175   -1.175
##     mrtv02_prc_w|2   -0.785    0.052  -14.960    0.000   -0.785   -0.785
##     mrtv02_prc_w|3   -0.322    0.048   -6.743    0.000   -0.322   -0.322
##     mrtv02_prc_w|4    0.344    0.048    7.188    0.000    0.344    0.344
##     mrtv02_prc_n|1   -1.381    0.067  -20.519    0.000   -1.381   -1.381
##     mrtv02_prc_n|2   -0.954    0.055  -17.193    0.000   -0.954   -0.954
##     mrtv02_prc_n|3   -0.473    0.049   -9.700    0.000   -0.473   -0.473
##     mrtv02_prc_n|4    0.517    0.049   10.507    0.000    0.517    0.517
##     mrtv02_prf_f|1   -1.121    0.059  -18.914    0.000   -1.121   -1.121
##     mrtv02_prf_f|2   -0.874    0.054  -16.197    0.000   -0.874   -0.874
##     mrtv02_prf_f|3   -0.513    0.049  -10.434    0.000   -0.513   -0.513
##     mrtv02_prf_f|4    0.366    0.048    7.633    0.000    0.366    0.366
##     mrtv02_prf_t|1   -1.303    0.065  -20.178    0.000   -1.303   -1.303
##     mrtv02_prf_t|2   -0.574    0.050  -11.530    0.000   -0.574   -0.574
##     mrtv02_prf_t|3    0.318    0.048    6.669    0.000    0.318    0.318
##     mrtv02_prf_t|4    1.102    0.059   18.741    0.000    1.102    1.102
##     mrtv02_prf_w|1   -0.795    0.053  -15.100    0.000   -0.795   -0.795
##     mrtv02_prf_w|2   -0.271    0.047   -5.703    0.000   -0.271   -0.271
##     mrtv02_prf_w|3    0.553    0.050   11.165    0.000    0.553    0.553
##     mrtv02_prf_w|4    1.532    0.073   20.855    0.000    1.532    1.532
##     mrtv02_prf_n|1   -0.667    0.051  -13.120    0.000   -0.667   -0.667
##     mrtv02_prf_n|2    0.153    0.047    3.246    0.001    0.153    0.153
##     mrtv02_prf_n|3    0.965    0.056   17.323    0.000    0.965    0.965
##     mrtv02_prf_n|4    1.779    0.087   20.510    0.000    1.779    1.779
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .mrtv02_prc_ffr    0.425                               0.425    0.425
##    .mrtv02_prc_tln    0.488                               0.488    0.488
##    .mrtv02_prc_wpr    0.292                               0.292    0.292
##    .mrtv02_prc_ntw    0.340                               0.340    0.340
##    .mrtv02_prf_ffr    0.333                               0.333    0.333
##    .mrtv02_prf_tln    0.579                               0.579    0.579
##    .mrtv02_prf_wpr   -0.090                              -0.090   -0.090
##    .mrtv02_prf_ntw    0.730                               0.730    0.730
##     perc_merit        0.575    0.044   13.145    0.000    1.000    1.000
##     perc_nmerit       0.708    0.077    9.254    0.000    1.000    1.000
##     pref_merit        0.667    0.055   12.183    0.000    1.000    1.000
##     pref_nmerit       1.090    0.307    3.554    0.000    1.000    1.000
## 
## Scales y*:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     mrtv02_prc_ffr    1.000                               1.000    1.000
##     mrtv02_prc_tln    1.000                               1.000    1.000
##     mrtv02_prc_wpr    1.000                               1.000    1.000
##     mrtv02_prc_ntw    1.000                               1.000    1.000
##     mrtv02_prf_ffr    1.000                               1.000    1.000
##     mrtv02_prf_tln    1.000                               1.000    1.000
##     mrtv02_prf_wpr    1.000                               1.000    1.000
##     mrtv02_prf_ntw    1.000                               1.000    1.000

1.3 Version 03: orden aleatorio

model03 <- 'perc_merit=~meritv03_perc_effort+meritv03_perc_talent 
            perc_nmerit=~meritv03_perc_wpart+meritv03_perc_netw  
            pref_merit=~meritv03_pref_effort+meritv03_pref_talent 
            pref_nmerit=~meritv03_pref_wpart+meritv03_pref_netw'

fit3 <- cfa(model = model03,data = dat03,ordered = c("meritv03_perc_effort","meritv03_perc_talent",
                                                     "meritv03_perc_wpart","meritv03_perc_netw",
                                                     "meritv03_pref_effort","meritv03_pref_talent",
                                                     "meritv03_pref_wpart","meritv03_pref_netw"))

summary(fit3,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit3,what = "std",thresholds = FALSE, intercepts = FALSE)

## lavaan 0.6-4 ended normally after 36 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                         46
## 
##   Number of observations                           712
## 
##   Estimator                                       DWLS      Robust
##   Model Fit Test Statistic                      41.633      63.336
##   Degrees of freedom                                14          14
##   P-value (Chi-square)                           0.000       0.000
##   Scaling correction factor                                  0.680
##   Shift parameter                                            2.116
##     for simple second-order correction (Mplus variant)
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             3012.318    2326.099
##   Degrees of freedom                                28          28
##   P-value                                        0.000       0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.991       0.979
##   Tucker-Lewis Index (TLI)                       0.981       0.957
## 
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.053       0.070
##   90 Percent Confidence Interval          0.035  0.072       0.053  0.088
##   P-value RMSEA <= 0.05                          0.375       0.026
## 
##   Robust RMSEA                                                  NA
##   90 Percent Confidence Interval                                NA     NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.042       0.042
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model        Unstructured
##   Standard Errors                           Robust.sem
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit =~                                                         
##     mrtv03_prc_ffr    1.000                               0.704    0.704
##     mrtv03_prc_tln    0.929    0.140    6.628    0.000    0.654    0.654
##   perc_nmerit =~                                                        
##     mrtv03_prc_wpr    1.000                               0.807    0.807
##     mrtv03_prc_ntw    1.105    0.116    9.508    0.000    0.892    0.892
##   pref_merit =~                                                         
##     mrtv03_prf_ffr    1.000                               0.658    0.658
##     mrtv03_prf_tln    0.897    0.099    9.029    0.000    0.591    0.591
##   pref_nmerit =~                                                        
##     mrtv03_prf_wpr    1.000                               0.781    0.781
##     mrtv03_prf_ntw    0.987    0.169    5.839    0.000    0.771    0.771
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit ~~                                                         
##     perc_nmerit      -0.025    0.029   -0.866    0.386   -0.044   -0.044
##     pref_merit        0.212    0.033    6.420    0.000    0.457    0.457
##     pref_nmerit       0.164    0.033    4.915    0.000    0.298    0.298
##   perc_nmerit ~~                                                        
##     pref_merit        0.265    0.035    7.614    0.000    0.500    0.500
##     pref_nmerit      -0.037    0.030   -1.228    0.219   -0.059   -0.059
##   pref_merit ~~                                                         
##     pref_nmerit       0.095    0.032    2.943    0.003    0.185    0.185
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .mrtv03_prc_ffr    0.000                               0.000    0.000
##    .mrtv03_prc_tln    0.000                               0.000    0.000
##    .mrtv03_prc_wpr    0.000                               0.000    0.000
##    .mrtv03_prc_ntw    0.000                               0.000    0.000
##    .mrtv03_prf_ffr    0.000                               0.000    0.000
##    .mrtv03_prf_tln    0.000                               0.000    0.000
##    .mrtv03_prf_wpr    0.000                               0.000    0.000
##    .mrtv03_prf_ntw    0.000                               0.000    0.000
##     perc_merit        0.000                               0.000    0.000
##     perc_nmerit       0.000                               0.000    0.000
##     pref_merit        0.000                               0.000    0.000
##     pref_nmerit       0.000                               0.000    0.000
## 
## Thresholds:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     mrtv03_prc_f|1   -1.185    0.061  -19.363    0.000   -1.185   -1.185
##     mrtv03_prc_f|2   -0.448    0.049   -9.179    0.000   -0.448   -0.448
##     mrtv03_prc_f|3   -0.056    0.047   -1.198    0.231   -0.056   -0.056
##     mrtv03_prc_f|4    0.653    0.051   12.839    0.000    0.653    0.653
##     mrtv03_prc_t|1   -1.207    0.062  -19.519    0.000   -1.207   -1.207
##     mrtv03_prc_t|2   -0.348    0.048   -7.250    0.000   -0.348   -0.348
##     mrtv03_prc_t|3    0.271    0.048    5.686    0.000    0.271    0.271
##     mrtv03_prc_t|4    1.029    0.057   17.971    0.000    1.029    1.029
##     mrtv03_prc_w|1   -1.244    0.063  -19.768    0.000   -1.244   -1.244
##     mrtv03_prc_w|2   -0.780    0.053  -14.832    0.000   -0.780   -0.780
##     mrtv03_prc_w|3   -0.401    0.048   -8.290    0.000   -0.401   -0.401
##     mrtv03_prc_w|4    0.274    0.048    5.760    0.000    0.274    0.274
##     mrtv03_prc_n|1   -1.395    0.068  -20.499    0.000   -1.395   -1.395
##     mrtv03_prc_n|2   -1.041    0.058  -18.095    0.000   -1.041   -1.041
##     mrtv03_prc_n|3   -0.593    0.050  -11.824    0.000   -0.593   -0.593
##     mrtv03_prc_n|4    0.234    0.047    4.939    0.000    0.234    0.234
##     mrtv03_prf_f|1   -1.495    0.072  -20.740    0.000   -1.495   -1.495
##     mrtv03_prf_f|2   -1.207    0.062  -19.519    0.000   -1.207   -1.207
##     mrtv03_prf_f|3   -0.752    0.052  -14.410    0.000   -0.752   -0.752
##     mrtv03_prf_f|4    0.131    0.047    2.771    0.006    0.131    0.131
##     mrtv03_prf_t|1   -1.368    0.067  -20.399    0.000   -1.368   -1.368
##     mrtv03_prf_t|2   -0.648    0.051  -12.766    0.000   -0.648   -0.648
##     mrtv03_prf_t|3    0.088    0.047    1.872    0.061    0.088    0.088
##     mrtv03_prf_t|4    0.799    0.053   15.112    0.000    0.799    0.799
##     mrtv03_prf_w|1   -0.911    0.055  -16.615    0.000   -0.911   -0.911
##     mrtv03_prf_w|2   -0.224    0.047   -4.715    0.000   -0.224   -0.224
##     mrtv03_prf_w|3    0.568    0.050   11.386    0.000    0.568    0.568
##     mrtv03_prf_w|4    1.454    0.070   20.663    0.000    1.454    1.454
##     mrtv03_prf_n|1   -0.785    0.053  -14.902    0.000   -0.785   -0.785
##     mrtv03_prf_n|2    0.116    0.047    2.471    0.013    0.116    0.116
##     mrtv03_prf_n|3    0.790    0.053   14.972    0.000    0.790    0.790
##     mrtv03_prf_n|4    1.667    0.080   20.723    0.000    1.667    1.667
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .mrtv03_prc_ffr    0.504                               0.504    0.504
##    .mrtv03_prc_tln    0.572                               0.572    0.572
##    .mrtv03_prc_wpr    0.349                               0.349    0.349
##    .mrtv03_prc_ntw    0.205                               0.205    0.205
##    .mrtv03_prf_ffr    0.567                               0.567    0.567
##    .mrtv03_prf_tln    0.651                               0.651    0.651
##    .mrtv03_prf_wpr    0.390                               0.390    0.390
##    .mrtv03_prf_ntw    0.406                               0.406    0.406
##     perc_merit        0.496    0.079    6.254    0.000    1.000    1.000
##     perc_nmerit       0.651    0.072    9.056    0.000    1.000    1.000
##     pref_merit        0.433    0.060    7.268    0.000    1.000    1.000
##     pref_nmerit       0.610    0.107    5.714    0.000    1.000    1.000
## 
## Scales y*:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     mrtv03_prc_ffr    1.000                               1.000    1.000
##     mrtv03_prc_tln    1.000                               1.000    1.000
##     mrtv03_prc_wpr    1.000                               1.000    1.000
##     mrtv03_prc_ntw    1.000                               1.000    1.000
##     mrtv03_prf_ffr    1.000                               1.000    1.000
##     mrtv03_prf_tln    1.000                               1.000    1.000
##     mrtv03_prf_wpr    1.000                               1.000    1.000
##     mrtv03_prf_ntw    1.000                               1.000    1.000

1.4 Version 04: muestra completa

dat04 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv01"),starts_with("meritv02"),starts_with("meritv03_p"))

dat04$perc_effort <- rowSums(dat04[,c(matches(match = "perc_effort",vars = names(dat04)))],na.rm = TRUE)
dat04$perc_talent <- rowSums(dat04[,c(matches(match = "perc_talent",vars = names(dat04)))],na.rm = TRUE)
dat04$perc_wpart  <- rowSums(dat04[,c(matches(match = "perc_wpart" ,vars = names(dat04)))],na.rm = TRUE)
dat04$perc_netw   <- rowSums(dat04[,c(matches(match = "perc_netw"  ,vars = names(dat04)))],na.rm = TRUE)

dat04$pref_effort <- rowSums(dat04[,c(matches(match = "pref_effort",vars = names(dat04)))],na.rm = TRUE)
dat04$pref_talent <- rowSums(dat04[,c(matches(match = "pref_talent",vars = names(dat04)))],na.rm = TRUE)
dat04$pref_wpart  <- rowSums(dat04[,c(matches(match = "pref_wpart" ,vars = names(dat04)))],na.rm = TRUE)
dat04$pref_netw   <- rowSums(dat04[,c(matches(match = "pref_netw"  ,vars = names(dat04)))],na.rm = TRUE)

model04 <- 'perc_merit=~perc_effort+perc_talent 
            perc_nmerit=~perc_wpart+perc_netw  
            pref_merit=~pref_effort+pref_talent 
            pref_nmerit=~pref_wpart+pref_netw'

fit4 <- cfa(model = model04,data = dat04)
summary(fit4,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit4,what = "std",thresholds = FALSE, intercepts = FALSE)

## lavaan 0.6-4 ended normally after 43 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                         22
## 
##   Number of observations                          2236
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                     209.300
##   Degrees of freedom                                14
##   P-value (Chi-square)                           0.000
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             6899.153
##   Degrees of freedom                                28
##   P-value                                        0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.972
##   Tucker-Lewis Index (TLI)                       0.943
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)             -27655.437
##   Loglikelihood unrestricted model (H1)     -27550.787
## 
##   Number of free parameters                         22
##   Akaike (AIC)                               55354.875
##   Bayesian (BIC)                             55480.549
##   Sample-size adjusted Bayesian (BIC)        55410.651
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.079
##   90 Percent Confidence Interval          0.070  0.089
##   P-value RMSEA <= 0.05                          0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.029
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model          Structured
##   Standard Errors                             Standard
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit =~                                                         
##     perc_effort       1.000                               1.121    0.750
##     perc_talent       0.873    0.033   26.293    0.000    0.979    0.759
##   perc_nmerit =~                                                        
##     perc_wpart        1.000                               1.246    0.818
##     perc_netw         1.038    0.030   34.064    0.000    1.294    0.901
##   pref_merit =~                                                         
##     pref_effort       1.000                               1.179    0.809
##     pref_talent       0.797    0.026   30.459    0.000    0.939    0.704
##   pref_nmerit =~                                                        
##     pref_wpart        1.000                               1.054    0.829
##     pref_netw         0.851    0.042   20.250    0.000    0.896    0.756
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit ~~                                                         
##     perc_nmerit       0.535    0.042   12.817    0.000    0.383    0.383
##     pref_merit        0.985    0.049   20.205    0.000    0.745    0.745
##     pref_nmerit       0.579    0.039   14.949    0.000    0.490    0.490
##   perc_nmerit ~~                                                        
##     pref_merit        1.029    0.050   20.443    0.000    0.701    0.701
##     pref_nmerit       0.443    0.037   11.923    0.000    0.337    0.337
##   pref_merit ~~                                                         
##     pref_nmerit       0.543    0.038   14.193    0.000    0.437    0.437
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .perc_effort       0.977    0.050   19.636    0.000    0.977    0.437
##    .perc_talent       0.703    0.037   18.900    0.000    0.703    0.423
##    .perc_wpart        0.767    0.044   17.383    0.000    0.767    0.331
##    .perc_netw         0.386    0.042    9.148    0.000    0.386    0.187
##    .pref_effort       0.733    0.041   17.817    0.000    0.733    0.345
##    .pref_talent       0.897    0.035   25.814    0.000    0.897    0.504
##    .pref_wpart        0.505    0.053    9.616    0.000    0.505    0.313
##    .pref_netw         0.603    0.041   14.843    0.000    0.603    0.429
##     perc_merit        1.258    0.072   17.381    0.000    1.000    1.000
##     perc_nmerit       1.552    0.076   20.556    0.000    1.000    1.000
##     pref_merit        1.390    0.069   20.143    0.000    1.000    1.000
##     pref_nmerit       1.111    0.068   16.307    0.000    1.000    1.000